II Paisaje y ciudad Su estudio arqueológico
II. PAISAJE Y CIUDAD SU ESTUDIO ARQUEOLÓGICO 1 Estudios sobre paisaje en Arqueología.
II.1.1. El espacio como objeto de estudio en el Occidente contemporáneo.
7.2
Similar to previous chapters, this second section is about the phase transition. The STS chapter has examined, with the evolution of the social fabric, the default flow. The SMS changes the default flow by innovation with organizations, creating a directed flow. What remains is constructive flow allowing a systematic approach to radical change. A constructive flow requires an understanding of how the meta-‐ model and the phase transition relate. This is reached by describing the Agile-‐ Enterprise Architecture Planning (ÆIP) grid. The ÆIP grid leaves still several practical problems unanswered that become the topic of the ÆIP architecture in the next chapter on the methodology.
The conversion patterns are the way to connect the meta-‐model to the phase transition. I have considered three conversion patterns so far for only the Establishing model: acting individuals, bootstrapping groups and leapfrogging organizations. The three conversion patterns already show a scale transition. The level of scale relates to the phase transitions, where the relation between individuals are essential during the premature phase, groups become essential during the incubation phase and organizations become essential during the growth phase.
Two more scales need to be elaborated for the phase transition. A complex network of organizations is recognized during the maturity phase of an innovation. Some organizations may exist on their own while others form a cluster or a chain. In relation to the scale, it needs to be recognized how the network of organizations is
local, being in local to a city, local to a province or local to a nation. The local scale stands in contrast to the global scale of the enrichment phase. The effect is an absorption into the social fabric, allowing us to focus back to the individual. With five stages of the phase transition and four novelty models, a total of twenty conversion patterns create the ÆIP grid (Figure 7.3).breakthrough
Figure 7.3 The content of the ÆIP grid showing the relation between phase transition and meta-‐
model
In the ÆIP grid, three different regulations exist for each conversion pattern. Some patterns are spontaneous. They are side effects of the other activities in a stage of the phase transition. In Table 7.1, the spontaneous patterns are gray. To move from spontaneous to control requires one hybrid pattern where regulation is reached by coordination. Each novelty model has one such pattern, and they show a staircase like the structure in the meta-‐model. The effect is that for earlier stages, more of the novelty regulations are happening as side effects and do not require management. To give the full picture of the 20 conversion patterns, each subsection looks again at the separate novelty models, starting with the Cohering model. By considering the conversion patterns for each novelty model, it can be argued how conversion for radical change truly happens. Before explaining the conversion patterns, I would like to take a moment to elaborate the choice of the name ÆIP and why this is relevant.
The rise of Agile-‐Enterprise
7.2.1
The inspiration for the name ÆIP comes from Enterprise Resource Planning (ERP). The reason to choose that name is because of the historical similarities. Before ERP existed, companies used to have an invisible barrier to manage day-‐to-‐day operations as only a few accountants had a holistic view of the companies' resources.
Today's visionary leaders are in a similar position to how super accountants used to be.
The problem with not understanding how some visionary leaders create innovation is that it results quickly in personal cult and celebrity status, which are not a guarantee for success. Moreover, celebrities make the market nervous. The well-‐ known example was Steve Jobs’ health and the direct influence that it had on the stock of Apple. Just as ERP did not replace super accountants, the Agile-‐Enterprise Innovation Planning (ÆIP) does not replace visionary leaders, but is intended to make the system more accountable. Applegate et al. (2003, p 232) introduce the Agile-‐Enterprise (Æ) as an Enterprise Architecture specifically designed to deal with innovation. In the first subsection, I consider the Æ architecture. In the second subsection, I discuss the relationship between IT governance and Æ.
Agile and the on-‐demand enterprise 7.2.1.1
Applegate et al. introduce the Æ architecture in the sixth edition of their book and provide more detail in the next edition (ibid. 2006 p. 58-‐71). It starts from a hybrid organization combining the benefits of a lean enterprise with an agile startup. Details are given in contrast to the classic hierarchical control (Figure 7.4). A separation is made between management processes and operational processes. In the classical hierarchical control, no feed-‐forward exists to adapt to changes. The new on demand control of Æ starts looking much more like a novelty regulation system.
Figure 7.4 The Æ described as an on demand enterprise controls (from Applegate 2006)
I will not go into the details of the guidelines created for each control element. They are guidelines and don't deal with the complexity of the meta-‐model. They are, however, created in such a way that they can better respond to a complex adaptive environment. Instead of guidelines, the agency and mediation of the ÆIP grid is required. Because all the regulations become complex, it is essential to have IT governance of the enterprise.
IT governance of Enterprises 7.2.1.2
The challenge to create a useful tool with the ÆIP grid becomes an IT governance problem. IT governance (Figure 7.5) shows the systematic method of developing IT of an enterprise. As with any organization the values of the stakeholders are essential. They are required for an IT strategy alignment. The alignment creates an insight on the values that the IT delivers, which results in a risk management of the IT. The risk management affects the resource management, while the resources affect the IT strategy alignment. This creates a loop. The IT strategy alignment is the most essential part of the governance and needs some more explanation.
Figure 7.5 Simplified IT governance model
Henderson and Venkatraman (1993) identify four dominant IT alignment patterns in two categories (Figure 7.6). The categories determine how to move across the organizational structure. The first category is "business strategy as driver". It begins with business strategy and can go in two directions to build Information Systems (IS) support. One direction is "strategy execution" that uses the organizational infrastructure to design IS support. The other direction is "technology transformation", using "IT strategy" – exploring state-‐of-‐art technology – to integrate the external infrastructure to design the IT support. The second category is "IT strategy as enabler" and uses IT strategy to design the organizational infrastructure. Once again, two paths exist. Now the "competitive potential" of the business strategy can lead to the organizational infrastructure or "service level", going across IS support can reach it.
In all cases the IT alignment is reached by going from one pattern of the company to another, using a third pattern as leverage. Putting IT-‐alignment in practice is still a tricky management process and has led to a lot of criticism. Chan and Reich (2007) have made a survey to gather the negative remarks on IT alignment:
• Alignment research is mechanistic and fails to capture real life.
• Alignment is not possible if the business strategy is unknown or in process.
• Alignment is not desirable as an end in itself since the business must always change.
In my opinion, the problem with IT-‐alignment is the way that the context is first simplified to apply IT-‐alignment. If we keep complexity in place, the IT alignment can be very useful. In other words, we need to replace the classic IT alignment by a self-‐organizing alignment by collective intelligence. How IT alignment by collective intelligence becomes possible should become clearer with the late stage of the maturity phase. Like previous phases, the later part of one phase shows the agency of the next phase (prototypic concept -‐> prototype, niche product -‐> open product and product framework -‐> support framework). The transition is now from collective intelligence to an alternative artificial intelligence. It aligns with the Internet Innovation waves (Section 5.2.10). Wave 5.0 is about collective intelligence, while wave 6.0 is about technological singularity and artificial intelligence.
An estimate was given for when wave 5.0 and wave 6.0 would get into a growth phase and it is still decades away (2030-‐2040 for wave 5.0 and 2040-‐2050 for wave 6.0) Such estimates can, of course, be wrong depending on all kinds of influences in the social fabric that can free the development or accelerate the development. The essence is that both waves are currently in a prematurity phase and so research on it should give us more insight. The research on ÆIP is an attempt to gain insight. Notice that the ÆIP is part of the Agile-‐Enterprise, which is wave 4.0. In other words, not all parts of the ÆIP grid need to get fully understood to become practical with ÆIP. In fact, for the ÆIP proof-‐of-‐concept in this PhD many of the later conversion patterns stay abstract.
Cohering and the SECI model
7.2.2
Knowledge conversion from, e.g. from implicit to explicit knowledge, exist in different patterns, where the patterns can give meta-‐information of the knowledge conversion. The conversion patterns of the Cohering model are about how individuals learn during the transformation the phase transition. Nonaka (1991) studies uncertainty during knowledge creation and constructs the conversion patterns, which he called the SECI model. SECI is a reference to the four conversion patterns of the model:
• Socialization: from tacit to tacit, which is transferring knowledge between people without actually being able to express the knowledge. It is like learning from a master-‐apprentice relation.
• Externalization: from tacit to explicit, which is learning by transforming experience into comprehensive forms that can be understood by others in the group. Still a lot of the knowledge will be tacit in the group.
• Combination: from explicit to explicit, which is learning by combining different pieces of explicit knowledge into a new concept. It is often reached by generalizing the knowledge between different groups.
• Internalization: from explicit to implicit, which is learning by absorbing the explicit knowledge. Nonaka actually calls it embodying knowledge (ibid).
The SECI model received criticism from Gourlay (2006, p 1421):
We can more simply refer to learning by doing on the one hand, and to designing new tasks on the other.
Note how Gourlay recognized the mastering (learning by doing) and modeling (designing). With the novelty theory it can be argued that both Nonaka and Gourlay are simply addressing the same problem from different angles and both are sound. The SECI model’s four conversion patterns can be elaborated as two effects: "shifting" knowledge (socialization and combination) and "transferring" knowledge (externalization and internalization). Nonaka (1998) created a drawing of the SECI model as a spiral construction (Figure 7.7), showing the two directions (shifting and transferring). That drawing also shows the scale going from individual, over groups to organizations. The images were reused in the ÆIP grid. Only a small adaptation is required to the SECI model to fit with the Cohering conversion patterns, which have five stages.
At the front of the phase transition, I have added a conversion pattern called "personalization". However, I don't shift the pictures as well. I do make a different picture for combination, related to an inter-‐organizational knowledge exchange. This change remains consistent with the spirit of the SECI model. Some retrofitting of the conversion patterns is required:
• Personalization: creation of tacit knowledge, an individual interacting with the environment can do it or two individuals in a master-‐apprentice relation.
• Socialization: creation of local knowledge. The knowledge cannot be shared across groups, because some part of the knowledge is still implicit, while other parts have become explicit (see Section 5.1.1 on local knowledge).
• Externalization: creation of abstract knowledge, which is knowledge that can be shared across groups by the abstract level of the knowledge. Often the integration requires a transformation of the knowledge to the local context.
• Combination: creation of standard knowledge, which is the transformation of different abstract knowledge to one standard. Like the metric system using the "meter" as a unit of length, in contrast to using more local units like "elbow".
• Internalization: creation of enriched environments, which allow actions without a need to understand how the enriched environment transforms the actions to the intended result.
Eventuating and the CACO design
7.2.3
The SECI model is the detailed analysis of many cases by an authority on knowledge creation. With only some minor adjustments, it fits the ÆIP grid. For eventuating, I have cases to demonstrate the conversion pattern. It is only my analysis based on participation research (see Chapter 10). Additional research is needed to validate the conversion patterns. The participation research made clear some unexpected
effects. For example, it is interesting to see the tech communities experiencing fast growth in physical event organizations like meetings and conferences. The tech communities are often called virtual communities, but in practice this creates a wrong perception.
The need for face-‐to-‐face interaction is essential to build a community. The reason for the physical events is evident. It is a moment for the collective to feel and experience the community. Often the best technical projects show extensive social activities. Having this moment to meet informally allows: exploring new ideas, discussing difficult problems, dissolving tensions, recognizing similarities and differences, etc. In other words, it helps to establish a culture. Due to the culture of the tech communities, the conference organization has been transformed significantly. A small group organizes classic event organizations. The tech communities use the collective intelligence more. Let me call this new kind of event organization Complex Adaptive Conference Organization (CACO).
CACO has a clear process, starting with an initial day of informal gathering and a training event, followed by the three-‐day main conference and ending on the last day of actual implementation with "code sprints". The first and last day are created in such a way to use the time optimally, while many participants are still in transit. During the three day conference there is a main track and open tracks. The main track has keynotes and parallel tracks, which look like a classic conference model. The main track is organized well in advance of the conference. The open tracks are spaces for people to create unplanned workshops. Such workshops can even popup from interaction during the event.
The participation research I did for this PhD was related to an open source project called Drupal. For the Drupal project, there was a CACO each year in EU and the USA. The USA would grow steadily with an almost exponential growth. The EU 2006 conference in Brussels that I co-‐organized had 150 people, which was just enough to have a CACO. In 2011, participation of over 3000 people in the USA conference was attained. Participants are comfortable in the use of high-‐tech Internet applications and are able to participate in the enriched environment created by the organizers. The experience with CACO gives a glimpse on what is considered possible for Eventuating. Just as the SECI conversion patterns show how knowledge creation changes, we also need to see group dynamic change during the phase transition with CACO.
During the premature phase, the scale of group dynamics does not exist and the conversion pattern of eventuating will be a side effect of individual activities. In table 7.1, this side effect is shown as gray cells. During the incubation phase the group dynamics are self-‐organizing by coordination. Only from the growth phase on do we see control of conversion. Let me consider details for each of the conversion pattern about group dynamics.
Networking to find grounding 7.2.3.1
The first conversion pattern for the Eventuating model relates to informal learning by individuals. In the CACO, it was observed how the growth depended on the familiarity with CACO which can explain the (almost) exponential growth. Almost half of the participants had experienced the event in the previous year and knew the dynamics of CACO. The other half had to familiarize themselves with the process. This group underwent the personalization of CACO by informal interactions with the experienced half of the conference. The new people were used to the main track, but often not to the open track. In a way, the main track functions as an interface for people to personalize themselves with the whole CACO structure.
The concept "unconference" can elaborate on the conversion pattern "networking". It is the observation that informal contacts during coffee breaks of conferences create important relationships. Therefore, the idea arose to make a conference become one extensive coffee break and have no formal conference organization. The idea has led to open structured meetings, like open spaces, camps (e.g. barcamp, govcamp, bizcamp, etc.), code jams (codejam, govjam, bizjam etc.), etc. In the CACO structure, the open meetings are called open tracks that have specific structure that are discussed in next subsection. The open tracks are already more advanced than the core of the informal gathering.
Coffee breaks allow networking between participants. Some structure can maximize networking, like the guidelines of "open space technology" (Owen 2008). In the CACO, the open spaces are self-‐organized by Internet interfaces. The drawback is that the participants need to familiarize themselves with such an enriched environment and it takes them time to cultivate such a high-‐tech culture. In the classic conference, there is often a "sponsor lounge" created in such a way so as to stimulate networking between participants and sponsors. As a side activity, you can actually see other people creating informal interaction too. Some conference organizations don't stop with the actual conference, but also plan the evening social activities.
For the Eventuating model, the networking is a conversion pattern during the prematurity phase as a side effect of personalization. In the conferences, this personalization is oriented towards other participants and the culture of the community, including the structure of the conference. In Section 11.1.2.2, I consider workshops in a design school that shows how students personalize themselves with the materials that they are going to use. In the case of CACO, the structure allows a classic conference to become an interface to more complex adaptive self-‐ organization. The activities are possible thanks to technology and methods